| Spvit: Enabling faster vision transformers via latency-aware soft token pruning Z Kong, P Dong, X Ma, X Meng, W Niu, M Sun, X Shen, G Yuan, B Ren, ... ECCV 2022, 2022 | 213 | 2022 |
| Sanity checks for lottery tickets: Does your winning ticket really win the jackpot? X Ma, G Yuan, X Shen, T Chen, X Chen, X Chen, N Liu, M Qin, S Liu, ... NeurIPS 2021, 2021 | 66 | 2021 |
| Lottery ticket preserves weight correlation: Is it desirable or not? N Liu, G Yuan, Z Che, X Shen, X Ma, Q Jin, J Ren, J Tang, S Liu, Y Wang ICML 2021, 2021 | 41 | 2021 |
| Npas: A compiler-aware framework of unified network pruning and architecture search for beyond real-time mobile acceleration Z Li, G Yuan, W Niu, P Zhao, Y Li, Y Cai, X Shen, Z Zhan, Z Kong, Q Jin, ... CVPR 2021 Oral, 2021 | 39* | 2021 |
| Improving dnn fault tolerance using weight pruning and differential crossbar mapping for reram-based edge ai G Yuan, Z Liao, X Ma, Y Cai, Z Kong, X Shen, J Fu, Z Li, C Zhang, H Peng, ... ISQED 2021, 2021 | 38 | 2021 |
| Deepmad: Mathematical architecture design for deep convolutional neural network X Shen, Y Wang, M Lin, Y Huang, H Tang, X Sun, Y Wang CVPR 2023, 2023 | 37 | 2023 |
| Peeling the onion: Hierarchical reduction of data redundancy for efficient vision transformer training Z Kong, H Ma, G Yuan, M Sun, Y Xie, P Dong, X Meng, X Shen, H Tang, ... AAAI 2023 Oral, 2023 | 24 | 2023 |
| Agile-Quant: Activation-Guided Quantization for Faster Inference of LLMs on the Edge X Shen, P Dong, L Lu, Z Kong, Z Li, M Lin, C Wu, Y Wang AAAI 2024, 2024 | 18 | 2024 |
| Edgeqat: Entropy and distribution guided quantization-aware training for the acceleration of lightweight llms on the edge X Shen, Z Kong, C Yang, Z Han, L Lu, P Dong, C Lyu, C Li, X Guo, Z Shu, ... arXiv preprint arXiv:2402.10787, 2024 | 13 | 2024 |
| Data level lottery ticket hypothesis for vision transformers X Shen, Z Kong, M Qin, P Dong, G Yuan, X Meng, H Tang, X Ma, Y Wang IJCAI 2023 Oral, 2023 | 11 | 2023 |
| Towards fast and accurate multi-person pose estimation on mobile devices X Shen, G Yuan, W Niu, X Ma, J Guan, Z Li, B Ren, Y Wang IJCAI 2021 Demo, 2021 | 11 | 2021 |
| LazyDiT: Lazy Learning for the Acceleration of Diffusion Transformers X Shen, Z Song, Y Zhou, B Chen, Y Li, Y Gong, K Zhang, H Tan, J Kuen, ... AAAI 2025, 2024 | 8 | 2024 |
| Pruning Foundation Models for High Accuracy without Retraining P Zhao, F Sun, X Shen, P Yu, Z Kong, Y Wang, X Lin EMNLP 2024 Findings, 2024 | 6 | 2024 |
| Exploring Token Pruning in Vision State Space Models Z Zhan, Z Kong, Y Gong, Y Wu, Z Meng, H Zheng, X Shen, S Ioannidis, ... NeurIPS 2024, 2024 | 6 | 2024 |
| Numerical Pruning for Efficient Autoregressive Models X Shen, Z Song, Y Zhou, B Chen, J Liu, R Zhang, RA Rossi, H Tan, T Yu, ... AAAI 2025, 2024 | 5 | 2024 |
| A survey of small language models C Van Nguyen, X Shen, R Aponte, Y Xia, S Basu, Z Hu, J Chen, M Parmar, ... arXiv preprint arXiv:2410.20011, 2024 | 5 | 2024 |
| Search for Efficient Large Language Models X Shen, P Zhao, Y Gong, Z Kong, Z Zhan, Y Wu, M Lin, C Wu, X Lin, ... NeurIPS 2024, 2024 | 5 | 2024 |
| TSLA: A Task-Specific Learning Adaptation for Semantic Segmentation on Autonomous Vehicles Platform J Liu, Z Kong, P Zhao, W Zeng, H Tang, X Shen, C Yang, W Zhang, ... Transactions on Computer-Aided Design, 2024 | 3 | 2024 |
| Rethinking Token Reduction for State Space Models Z Zhan, Y Wu, Z Kong, C Yang, Y Gong, X Shen, X Lin, P Zhao, Y Wang EMNLP 2024, 2024 | 3 | 2024 |
| HotaQ: Hardware Oriented Token Adaptive Quantization for Large Language Models X Shen, Z Han, L Lu, Z Kong, P Dong, Z Li, Y Xie, C Wu, M Leeser, P Zhao, ... Transactions on Computer-Aided Design, 2024 | 2 | 2024 |
Yanzhi WangNortheastern UniversityEmail yang diverifikasi di northeastern.edu